Deep Multi-Scale Separable Convolutional Network with Triple Attention Mechanism: A Novel Multi-Task Domain Adaptation Method for Intelligent Fault Diagnosis

2021 ◽  
pp. 115087
Author(s):  
Bo Zhao ◽  
Xianmin Zhang ◽  
Zhenhui Zhan ◽  
Qiqiang Wu
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Attention Mechanism ◽  
Intelligent Fault Diagnosis ◽  
Convolutional Network ◽  
Multi Scale ◽  
Adaptation Method

scholarly journals Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3937 ◽  
Author(s):  
Tengda Huang ◽  
Sheng Fu ◽  
Haonan Feng ◽  
Jiafeng Kuang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Time Domain ◽  
Attention Mechanism ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale

Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human feature extraction and prior knowledge, thereby achieving an end-to-end intelligent fault diagnosis model. However, the data for mechanical fault diagnosis in practical application are limited, the CNN model is too deep and too complex, making it prone to overfitting, and a model with too simple a structure and shallow layers cannot fully learn the effective features of the data. Convolutional filters with fixed window sizes are widely used in existing CNN models, which cannot flexibly select variable pivotal features. The model may be interfered with by redundant information in feature maps during training. Therefore, in this paper, a novel shallow multi-scale convolutional neural network with attention is proposed for bearing fault diagnosis. The shallow multi-scale convolutional neural network structure can fully learn the feature information of input data without overfitting. For the first time, a feature attention mechanism is developed for fault diagnosis to adaptively select features for classification more effectively, where the pivotal feature was emphasized, and the redundant feature was weakened through an attention mechanism. The time frequency representations as the input of the model were obtained from the vibration time domain signals, which contain the complete time domain and frequency domain information of the vibration signals. Compared with the current popular diagnostic methods, the results show that the proposed diagnostic method has fairly high accuracy, and its performance is superior to the existing methods. The average recognition accuracy was 99.86%, and the weak recognition rate of I-07 and I-14 labels was improved.

Intelligent Fault Diagnosis With Deep Adversarial Domain Adaptation

2021 ◽  
Vol 70 ◽  
pp. 1-9
Author(s):  
Yu Wang ◽  
Xiaojie Sun ◽  
Jie Li ◽  
Ying Yang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis

Double-level adversarial domain adaptation network for intelligent fault diagnosis

2020 ◽  
Vol 205 ◽  
pp. 106236 ◽  
Author(s):  
Jinyang Jiao ◽  
Jing Lin ◽  
Ming Zhao ◽  
Kaixuan Liang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis
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